arXiv:2605.25160v2 Announce Type: replace Abstract: GUI agents powered by large language models are advancing rapidly, creating urgent needs for evaluation and training based on realistic environments. However, directly doing so in real-world environments introduces some challenges that cannot be overlooked. Real-world environments are complex and uncontrollable, making it difficult to construct verifiable rewards and to save or reset states. Existing works prioritize reproducibility but are often limited to open-source apps or file-operation tasks for reliable reward building, leaving a persi
The rapid advancement of large language models necessitates better evaluation and training environments for GUI agents, leading to innovations like large-scale environmental synthesis to overcome real-world complexities.
Sophisticated readers should care because improved training and evaluation for GUI agents accelerate the development of autonomous systems capable of interacting with complex interfaces, impacting white-collar workflows.
The ability to synthesize realistic yet controllable environments for GUI agents allows for more robust training and testing, moving beyond the limitations of real-world or restricted open-source environments.
- · AI development platforms
- · SaaS providers adopting agents
- · Automation software companies
- · Manual workflow providers
- · Companies with static UI/UX
GUI agents will become more capable and reliable across a wider range of applications, increasing their commercial viability.
This capability will enable the automation of highly complex, multi-application tasks previously thought too difficult for AI.
The proliferation of such agents could lead to significant restructuring of service industries and professional workflows, demanding new forms of human-AI collaboration.
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Read at arXiv cs.AI